Protein Structure

Since the dawn of recorded history, and probably even before, men and women have been grasping at the mechanisms by which they themselves exist. Only relatively recently, did this grasp yield anything of substance, and only within the last several decades did the proteins play a pivotal role in this existence. In this expose on the topic of protein structure some of the current issues in this scientific field are discussed. The aim is that a non-expert can gain some appreciation for the intricacies involved, and in the current state of affairs. The expert meanwhile, we hope, can gain a deeper understanding of the topic

Pacific Symposium on Biocomputing 2023

The Pacific Symposium on Biocomputing (PSB) 2023 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2023 will be held on January 3-7, 2023 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2023 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field

Computational methodology for enhanced sensitivity analysis of gene regulatory networks

Biological computing is held towards a new era of processing platforms based on the bio-logical computer structures that are at the heart of biological systems with information processing capabilities. These bio-logical computer structures are mostly based on gene regulatory networks, mainly because their dynamics reminds the computer logic structures functioning. The use of these bio-structures is still in its early days since they are for the time being far less effective than their silicon counterparts. However, their use can be already exploited for a wide range of applications, covering pharmacological, medical and industrial. In order to develop such applications, a precise design that is based on computational modelling is vital in the process of their implementation. Gene regulatory networks can be described as a chemical reacting systems. The dynamics of such systems is defined at the molecular level with a set of interacting reactions. The stochastic simulation algorithm can be used to generate the time evolution trajectories of each chemical species by firing each reaction according to a Monte-Carlo experiment. The main shortcoming of this approach is its computational complexity, which increases linearly with the total number of reactions that have to be simulated. When the number of reactions becomes too high, the stochastic simulation algorithm turns out to be impracticable. This is the case of certain gene regulatory networks, which can be either found in nature or can be artificially constructed. An additional problem lies in the fact that reactions in such networks can often occur at different time scales, which can differ by many orders of magnitude. Such scenario occurs when gene regulatory networks contain multiple cis-regulatory binding sites, on which different transcription factors are able to bind non-cooperatively. The transcription factors binding occurs much faster than the average reactions in the gene expression, therefore, this time-scale gap needs to be accounted into the simulation. Moreover, the transcription control can be affected by specific dispositions of the bound transcription factors, which is only possible to simulate, if all the reactions that can produce the same dispositions are defined. The number of such reactions increases exponentially with the number of binding sites. In order to decrease the time complexity of the stochastic simulation algorithm for such gene regulatory networks, an alternative algorithm called the dynamic multi-scale stochastic algorithm (DMSSA) is proposed, in which the reactions involved in the transcription regulation can be simulated independently, by performing the stochastic simulation algorithm in a nested fashion. This is conditioned by the property of the set of reactions, describing the gene regulatory network, being divided into two subsets, i.e. a set of "fast" reactions, which occur frequently in a short time scale, and a set of "slow" reactions, which occur less frequently in longer time scales. This thesis demonstrates the equivalence between this approach and the standard stochastic simulation algorithm and shows its capabilities on two gene regulatory models, that are commonly used as examples in systems and synthetic biology. The thesis focuses on how to identify the most important input parameters of multi-scale models, that affect the system the most. This is a common practice during the design of bio-logical structures and can be achieved with the sensitivity analysis. It may be difficult to carry out such analysis for complex reaction networks exhibiting different time scales. In order to cope with this issue, an alternative computation of the elementary effects in the Morris screening method is proposed, which is able to sort all the model parameters, independently on their structural or time scale definitions, in order of importance, i.e. which parameter carries the largest influence on the response of the model. To ease the use of the simulation algorithm and to perform the sensitivity analysis, the thesis presents ParMSSA, an OpenCL based engine for performing parallel stochastic simulations on multi-core architectures. ParMSSA aims to accelerate the simulations, performed with our approach. ParMSSA is capable to run concurrently multiple instances of DMSSA, which are usually needed for reducing the noisy results of stochastic simulations. ParMSSA provides also a framework for performing the Morris screening experiment on reaction networks, which allows users to carry out the sensitivity analysis of observed systems. The simulation results provided by the ParMSSA can be easily interpreted and can be used to assess the robustness of the bio-logical computer structures. The proposed algorithms and the proposed simulation engine were applied on two case studies, i.e. on the Epstein-Barr virus genetic switch and on the synthetic repressilator with multiple transcription factor binding sites. The results of the sensitivity analysis of the repressilator revealed that larger numbers of binding sites increase the robustness of the system and thus the robustness of the oscillatory behaviour

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

XVI Agricultural Science Congress 2023: Transformation of Agri-Food Systems for Achieving Sustainable Development Goals

The XVI Agricultural Science Congress being jointly organized by the National Academy of Agricultural Sciences (NAAS) and the Indian Council of Agricultural Research (ICAR) during 10-13 October 2023, at hotel Le Meridien, Kochi, is a mega event echoing the theme “Transformation of Agri-Food Systems for achieving Sustainable Development Goals”. ICAR-Central Marine Fisheries Research Institute takes great pride in hosting the XVI ASC, which will be the perfect point of convergence of academicians, researchers, students, farmers, fishers, traders, entrepreneurs, and other stakeholders involved in agri-production systems that ensure food and nutritional security for a burgeoning population. With impeding challenges like growing urbanization, increasing unemployment, growing population, increasing food demands, degradation of natural resources through human interference, climate change impacts and natural calamities, the challenges ahead for India to achieve the Sustainable Development Goals (SDGs) set out by the United Nations are many. The XVI ASC will provide an interface for dissemination of useful information across all sectors of stakeholders invested in developing India’s agri-food systems, not only to meet the SDGs, but also to ensure a stable structure on par with agri-food systems around the world. It is an honour to present this Book of Abstracts which is a compilation of a total of 668 abstracts that convey the results of R&D programs being done in India. The abstracts have been categorized under 10 major Themes – 1. Ensuring Food & Nutritional Security: Production, Consumption and Value addition; 2. Climate Action for Sustainable Agri-Food Systems; 3. Frontier Science and emerging Genetic Technologies: Genome, Breeding, Gene Editing; 4. Livestock-based Transformation of Food Systems; 5. Horticulture-based Transformation of Food Systems; 6. Aquaculture & Fisheries-based Transformation of Food Systems; 7. Nature-based Solutions for Sustainable AgriFood Systems; 8. Next Generation Technologies: Digital Agriculture, Precision Farming and AI-based Systems; 9. Policies and Institutions for Transforming Agri-Food Systems; 10. International Partnership for Research, Education and Development. This Book of Abstracts sets the stage for the mega event itself, which will see a flow of knowledge emanating from a zeal to transform and push India’s Agri-Food Systems to perform par excellence and achieve not only the SDGs of the UN but also to rise as a world leader in the sector. I thank and congratulate all the participants who have submitted abstracts for this mega event, and I also applaud the team that has strived hard to publish this Book of Abstracts ahead of the event. I wish all the delegates and participants a very vibrant and memorable time at the XVI ASC